Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

Abinash Tripathy (Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India) and Santanu Kumar Rath (Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India)
Copyright: © 2017 |Pages: 19
DOI: 10.4018/IJRSDA.2017010104
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Abstract

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
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2. Literature Survey

Pang et al., have considered sentiment classification as a special case of topic based on categorization aspect with positive and negative sentiments (Pang, Lee, & Vaithyanathan, 2002). They have undertaken the experiment with three standard algorithms i.e., Naive Bayes classification, Maximum Entropy classification and Support Vector machine being applied over the n-gram technique.

Pang and Lee have labeled sentences in the document as subjective or objective (Pang & Lee, 2004). They have applied machine learning classifier to the subjective group which prevents polarity classification from considering any misleading data. They have explored extraction of methods on the basis of minimum-cut formulation, which provides an effective way for integration of inter-sentence level information with bag of words.

Matsumoto et al., have considered the syntactic relationship among words as a basis of document level sentiment analysis (Matsumoto, Takamura, & Okumura, 2005). In this paper, frequent word sub-sequence and dependency sub-trees are extracted from sentences and they act as feature for SVM algorithm. They have used unigram method, bigram method and combination of both the methods for classification.

Read have proposed a source of training data based on language used in conjunction with emotions, could function independent of topic, domain and time (Read, 2005). Naive Bayes (NB) and Support Vector Machine (SVM) algorithms are applied to classify the polarity of the dataset.

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